AI Rookies Labs: Beginning NLP with Orange is a hands-on, visual lab workbook that introduces students to natural language processing (NLP) using the no-code Orange data mining platform. Designed for high school, college, and independent learners, this book teaches core concepts such as text preprocessing, sentiment analysis, topic modeling, and text classification-without requiring any programming experience. Each lab guides readers through step-by-step workflows using drag-and-drop widge...

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Py...

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features\[\*\] Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision\[\*\] Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more\[\*\] Includes tips on optimizing and improvi...

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.Each chapter consists of several recipes needed to complete a single project, such as train...

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RKey FeaturesA comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and RCombine Tableau analytics and visualization with the power of R using this step-by-step guideWondering how R can be used with Tableau? This book is your one-stop solution.Book Descr...

Build intelligent end-to-end machine learning systems with PythonKey FeaturesUse scikit-learn and TensorFlow to train your machine learning modelsImplement popular supervised and unsupervised machine learning algorithms in PythonDiscover best practices for building production-grade machine learning systems from scratchBook DescriptionMachine learning enables systems to make predictions based on historical data. Python is one ...

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlowKey FeaturesWeave neural networks into linguistic applications across various platformsPerform NLP tasks and train its models using NLTK and TensorFlowBoost your NLP models with strong deep learning architectures such as CNNs and RNNsBook DescriptionNatural language processing (NLP) has found its application in various domains, such as web search...

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practices

Distributed Systems: Concurrency and Consistency explores the gray area of distributed systems and draws a map of weak consistency criteria, identifying several families and demonstrating how these may be implemented into a programming language. Unlike their sequential counterparts, distributed systems are much more difficult to design, and are therefore prone to problems. On a large scale, usability reminiscent of sequential consistency, which would provide the same global view to all use...

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnKey FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

The Unified Modeling Language (UML) is a methodology to document the analysis and design of the software development process. Through the use of standard diagrams for such concepts as use cases, interactions, and collaborations, among many others, "Fast Track UML 2.0" explores the modeling techniques and the changes since the prior UML 1.3 standard.This book presents a distillation of the contents of the UML Superstructure document. It will capture the essential information contain...

A reliable, cost-effective approach to extracting priceless business information from all sources of textExcavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using ...